Spatiotemporal graphical modeling for cyber-physical systems

Cyber-Physical Systems (CPSs) are combinations of physical processes and network computation. Modern CPSs such as smart buildings, power plants, transportation networks, and power-grids have shown tremendous potential for increased efficiency, robustness, and resilience. However, such modern CPSs encounter a large variety of physical faults and cyber anomalies, and in many cases are vulnerable to catastrophic fault propagation scenarios due to strong connectivity among their sub-systems. To address these issues, this study proposes a graphical modeling framework to monitor and predict the performance of CPSs in a scalable and robust way. This thesis investigates on two critical CPS applications to evaluate the effectiveness of this proposed framework, namely (i) health monitoring of highway traffic sensors and (ii) building energy consumption prediction. In highway traffic sensor networks, accurate traffic sensor data is essential for traffic operation management systems and acquisition of real-time traffic surveillance data depends heavily on the reliability of the physical systems. Therefore, detecting the health status of the sensors in a traffic sensor network is critical for the departments of transportation as well as other public and private entities, especially in the circumstances where real-time decision making is required. With the purpose of efficiently determining the traffic network status and identifying failed sensor(s), this study proposes a cost-effective spatiotemporal graphical modeling approach called spatiotemporal pattern network (STPN). Traffic speed and volume measurement sensors are used in this work to formulate and analyze the proposed sensor health monitoring system. The historical time-series data from the networked traffic sensors on the Interstate 35 (I-35) within the state of Iowa is used for validation. Based on the validation results, this study demonstrates that the proposed graphical modeling approach can: (i) extract spatiotemporal dependencies among the different sensors which lead to an efficient graphical representation of the sensor network in the information space, and (ii) distinguish and quantify a sensor issue by leveraging the extracted spatiotemporal relationship of the candidate sensor(s) to the other sensors in the network. In the building energy consumption prediction case, we consider the fact that energy performance of buildings is primarily affected by the heat exchange with the building outer skin and the surrounding environment. In addition, it is a common practice in building energy simulation (BES) to predict energy usage with a variable degree of accuracy. Therefore, to account for accurate building energy consumption, especially in urban environments with a lot of anthropogenic heat sources, it is necessary to consider the microclimate conditions around the building. These conditions are influenced by the immediate environment, such as surrounding buildings, hard surfaces, and trees. Moreover, deployment of sensors to monitor the microclimate information of a building can be quite challenging and therefore, not scalable. Instead of applying local weather data directly on building energy simulation (BES) tools, this work proposes a spatiotemporal pattern network (STPN) based machine learning framework to predict the microclimate information based on the local weather station, which leads to better energy consumption prediction in buildings

Statistical Mechanics and Information-Theoretic Perspectives on Complexity in the Earth System

Peer reviewedPublisher PD

An Immune Inspired Approach to Anomaly Detection

The immune system provides a rich metaphor for computer security: anomaly detection that works in nature should work for machines. However, early artificial immune system approaches for computer security had only limited success. Arguably, this was due to these artificial systems being based on too simplistic a view of the immune system. We present here a second generation artificial immune system for process anomaly detection. It improves on earlier systems by having different artificial cell types that process information. Following detailed information about how to build such second generation systems, we find that communication between cells types is key to performance. Through realistic testing and validation we show that second generation artificial immune systems are capable of anomaly detection beyond generic system policies. The paper concludes with a discussion and outline of the next steps in this exciting area of computer security.Comment: 19 pages, 4 tables, 2 figures, Handbook of Research on Information Security and Assuranc

Steganographer Identification

Conventional steganalysis detects the presence of steganography within single objects. In the real-world, we may face a complex scenario that one or some of multiple users called actors are guilty of using steganography, which is typically defined as the Steganographer Identification Problem (SIP). One might use the conventional steganalysis algorithms to separate stego objects from cover objects and then identify the guilty actors. However, the guilty actors may be lost due to a number of false alarms. To deal with the SIP, most of the state-of-the-arts use unsupervised learning based approaches. In their solutions, each actor holds multiple digital objects, from which a set of feature vectors can be extracted. The well-defined distances between these feature sets are determined to measure the similarity between the corresponding actors. By applying clustering or outlier detection, the most suspicious actor(s) will be judged as the steganographer(s). Though the SIP needs further study, the existing works have good ability to identify the steganographer(s) when non-adaptive steganographic embedding was applied. In this chapter, we will present foundational concepts and review advanced methodologies in SIP. This chapter is self-contained and intended as a tutorial introducing the SIP in the context of media steganography.Comment: A tutorial with 30 page

Misbehaviour Prediction for Autonomous Driving Systems

Deep Neural Networks (DNNs) are the core component of modern autonomous driving systems. To date, it is still unrealistic that a DNN will generalize correctly in all driving conditions. Current testing techniques consist of offline solutions that identify adversarial or corner cases for improving the training phase, and little has been done for enabling online healing of DNN-based vehicles. In this paper, we address the problem of estimating the confidence of DNNs in response to unexpected execution contexts with the purpose of predicting potential safety-critical misbehaviours such as out of bound episodes or collisions. Our approach SelfOracle is based on a novel concept of self-assessment oracle, which monitors the DNN confidence at runtime, to predict unsupported driving scenarios in advance. SelfOracle uses autoencoder and time-series-based anomaly detection to reconstruct the driving scenarios seen by the car, and determine the confidence boundary of normal/unsupported conditions. In our empirical assessment, we evaluated the effectiveness of different variants of SelfOracle at predicting injected anomalous driving contexts, using DNN models and simulation environment from Udacity. Results show that, overall, SelfOracle can predict 77% misbehaviours, up to 6 seconds in advance, outperforming the online input validation approach of DeepRoad by a factor almost equal to 3.Comment: 11 page